1. Field of the Invention
The present invention relates to an anomaly detecting method and an anomaly detecting apparatus that can accomplish early detection of any anomaly on the basis of multi-dimensional time series data outputted by a plant or facility.
2. Description of the Related Art
Electric power companies are utilizing waste heat from gas turbines or like energy sources for supplying warm water for use in district heating or high- or low-pressure steam to factories. Petrochemical companies operate gas turbines or the like as power supply plants. In such plants and facilities using gas turbines, it is extremely important to detect any anomaly or any sign of anomaly in their equipment in order to minimize the consequent social damage.
Not only gas turbines and steam turbines, but also water wheels in hydropower stations, nuclear reactors in nuclear power stations, windmills of wind power stations, engines of aircraft and heavy machinery, railway rolling stock and tracks, escalators, elevators, medical diagnosing systems including MRI and X-ray CT and, at the individual machine or component level, mounted batteries, to cite but a few examples of numberless relevant cases, require such preventive maintenance against their deterioration and aging.
For this reason, a plurality of sensors are fitted to a facility or equipment to be monitored, and normal or anomaly is determined in accordance with monitoring standards for each individual sensor.
Japanese Patent Application Laid-Open No. 2011-70635 (Patent Document 1) discloses an anomaly detecting method to detect the presence or absence of anomaly on the basis of anomaly measurements calculated by comparison with normal data on past performance and generate a normal model by local subspace classification method. Since anomaly detecting method based on a normal model involves the problem that the sensitivity is affected by the quality of learned data, normal data has to be collected comprehensively and accurately. The comprehensiveness requirement can be met by extending the period of learning. Regarding accuracy, invasion of any anomaly would make it impossible to set an appropriate threshold and invite a drop in sensitivity, anomaly data should be automatically excluded from the learned data.
To meet this requirement, Japanese Patent Application Laid-Open No. 2011-70635 (Patent Document 1) discloses a method by which averages and variances of different features and different periods are used as the basis and learning data of periods deviating from this basis are excluded and another method by which learning data deviating more frequently than a threshold from a single-period waveform model for each feature is excluded. Patent Document 1 also discloses a method by which a plurality of normal models are generated by sampling normal data period by period, the presence or absence of any anomaly is determined by averaging a top few of anomaly measurements calculated by using the models, and learned data of periods containing anomaly values is excluded.